Download Udemy - Explainable Artificial Intelligence (XAI) with Python torrent

Udemy - Explainable Artificial Intelligence (XAI) with Python

Torrent:	Udemy - Explainable Artificial Intelligence (XAI) with Python
Description:	Explainable Artificial Intelligence (XAI) with Python https://DevCourseWeb.com MP4 \| Video: h264, 1280x720 \| Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning \| Language: English + srt \| Duration: 56 lectures (7h 56m) \| Size: 3.07 GB Simplified Way to Learn XAI What you'll learn Importance of XAI in modern world Differentiation of glass box, white box and black box ML models Categorization of XAI on the basis of their scope, agnosticity, data types and explanation techniques Trade-off between accuracy and interpretability Application of InterpretML package from Microsoft to generate explanations of ML models Need of counterfactual and contrastive explanations Working principles and mathematical modeling of XAI techniques like LIME, SHAP, DiCE, LRP, counterfactual and contrastive explanationss Application of XAI techniques like LIME, SHAP, DiCE, LRP to generate explanations for black-box models for tabular, textual, and image datasets. What-if tool from Google to analyze data points and to generate counterfactuals Requirements No programming experience needed. You will learn everything you need to know to apply XAI for generating explanations for ML models.
Downloads:	55
Category:	Other /Tutorials
Size:	3.3 GB
Show Files »	[ DevCourseWeb.com ] Udemy - Explainable Artificial Intelligence (XAI) with Python Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 01 - Introduction to XAI 001 XAI in Action.mp4 (103.6 MB) 001 XAI in Action_en.srt (13.0 KB) 002 Need and Importance of XAI.mp4 (79.5 MB) 002 Need and Importance of XAI_en.srt (11.8 KB) 003 By Design Interpretable Models Decision Tree Glass Box Models.mp4 (54.8 MB) 003 By Design Interpretable Models Decision Tree Glass Box Models_en.srt (11.9 KB) 004 By Design Interpretable Models Logistic Regression Glass Box Models.mp4 (50.6 MB) 004 By Design Interpretable Models Logistic Regression Glass Box Models_en.srt (11.5 KB) 005 Black Box Models Part-1.mp4 (30.5 MB) 005 Black Box Models Part-1_en.srt (6.8 KB) 006 Black Box Models Part-2.mp4 (45.1 MB) 006 Black Box Models Part-2_en.srt (7.8 KB) 007 XAI Categorization.mp4 (37.1 MB) 007 XAI Categorization_en.srt (7.8 KB) 02 - Demonstration of By Design Interpretable Models Glass Box 001 Demonstration of Glass Box Models Part-1.mp4 (51.8 MB) 001 Demonstration of Glass Box Models Part-1_en.srt (10.9 KB) 002 Demonstration of Glass Box Models Part-2.mp4 (35.3 MB) 002 Demonstration of Glass Box Models Part-2_en.srt (5.3 KB) 003 Need for Train-Test Split.mp4 (118.3 MB) 003 Need for Train-Test Split_en.srt (18.0 KB) 004 Techniques for Balancing the Dataset.mp4 (37.0 MB) 004 Techniques for Balancing the Dataset_en.srt (8.7 KB) 005 Code for Balancing the Dataset.mp4 (33.2 MB) 005 Code for Balancing the Dataset_en.srt (4.8 KB) 006 Quality Metrics for Classification Confusion Matrix, Precision, Recall, F1Score.mp4 (57.5 MB) 006 Quality Metrics for Classification Confusion Matrix, Precision, Recall, F1Score_en.srt (13.5 KB) 007 Demo of Data Exploration for Stroke Dataset.mp4 (65.5 MB) 007 Demo of Data Exploration for Stroke Dataset_en.srt (11.1 KB) 008 InterpretML Package.mp4 (56.9 MB) 008 InterpretML Package_en.srt (8.1 KB) 009 Demo for Logistic Regression Model Explanation.mp4 (76.2 MB) 009 Demo for Logistic Regression Model Explanation_en.srt (10.7 KB) 010 Demo for Decision Tree Classifier Explanation.mp4 (130.5 MB) 010 Demo for Decision Tree Classifier Explanation_en.srt (22.5 KB) 011 Explainable Boosting Classifier Working Principle.mp4 (40.3 MB) 011 Explainable Boosting Classifier Working Principle_en.srt (9.5 KB) 012 Demo for Explainable Boosting Classifier Explanaation.mp4 (77.8 MB) 012 Demo for Explainable Boosting Classifier Explanaation_en.srt (12.2 KB) external-assets-links.txt (0.5 KB) 03 - LIME (Local Interpretable Model Agnostic Explanations) 001 LIME Working Principle.mp4 (65.8 MB) 001 LIME Working Principle_en.srt (13.1 KB) 002 Mathematical Modelling of LIME Part-1.mp4 (50.2 MB) 002 Mathematical Modelling of LIME Part-1_en.srt (11.6 KB) 003 Mathematical Modelling of LIME Part-2.mp4 (55.8 MB) 003 Mathematical Modelling of LIME Part-2_en.srt (13.0 KB) 004 Demo of LIME for tabular Stroke Dataset.mp4 (93.3 MB) 004 Demo of LIME for tabular Stroke Dataset_en.srt (14.0 KB) 005 LIME Demonstration for textual dataset Part-1.mp4 (64.2 MB) 005 LIME Demonstration for textual dataset Part-1_en.srt (11.7 KB) 006 LIME Demonstration for textual dataset Part-2.mp4 (71.3 MB) 006 LIME Demonstration for textual dataset Part-2_en.srt (11.5 KB) 007 LIME Demonstration for textual dataset Part-3.mp4 (134.7 MB) 007 LIME Demonstration for textual dataset Part-3_en.srt (20.3 KB) 008 Recommended Practice Tasks.html (0.9 KB) external-assets-links.txt (0.6 KB) 04 - SHAP (SHapley Additive exPlanations) 001 SHAP Working Principle.mp4 (48.9 MB) 001 SHAP Working Principle_en.srt (12.2 KB) 002 Mathematical Modelling of SHAP Part-1.mp4 (37.3 MB) 002 Mathematical Modelling of SHAP Part-1_en.srt (8.1 KB) 003 Mathematical Modelling of SHAP Part-2.mp4 (53.7 MB) 003 Mathematical Modelling of SHAP Part-2_en.srt (10.9 KB) 004 Mathematical Modelling of SHAP Part-3.mp4 (76.4 MB) 004 Mathematical Modelling of SHAP Part-3_en.srt (16.5 KB) 005 SHAP Demonstration.mp4 (190.4 MB) 005 SHAP Demonstration_en.srt (25.1 KB) 006 Recommended Practice Tasks.html (0.7 KB) external-assets-links.txt (0.1 KB) 05 - Counterfactual Explanations 001 Working Principle of Counterfactual Explanations-1.mp4 (44.8 MB) 001 Working Principle of Counterfactual Explanations-1_en.srt (9.1 KB) 002 Working Principle of Counterfactual Explanations.mp4 (46.4 MB) 002 Working Principle of Counterfactual Explanations_en.srt (9.8 KB) 003 Mathematical Modelling of Counterfactual Explanations.mp4 (71.6 MB) 003 Mathematical Modelling of Counterfactual Explanations_en.srt (14.9 KB) 004 Global Counterfactuals.mp4 (26.9 MB) 004 Global Counterfactuals_en.srt (4.4 KB) 005 Demo of Counterfactual Explanations on Stroke Dataset.mp4 (172.8 MB) 005 Demo of Counterfactual Explanations on Stroke Dataset_en.srt (26.8 KB) 006 Recommended Practice Tasks.html (0.8 KB) external-assets-links.txt (0.1 KB) 06 - Google's What-if Tool (WIT) for AI fairness and Counterfactuals 001 Case Study-1 Demo of What-if Tool (WIT).mp4 (103.7 MB) 001 Case Study-1 Demo of What-if Tool (WIT)_en.srt (19.0 KB) 002 Case Study-2 Demo of What-if Tool (WIT).mp4 (96.0 MB) 002 Case Study-2 Demo of What-if Tool (WIT)_en.srt (16.2 KB) 003 Case Study-3 Demo of What-if Tool (WIT).mp4 (84.0 MB) 003 Case Study-3 Demo of What-if Tool (WIT)_en.srt (11.1 KB) 004 Case Study-4 Demo of What-if Tool (WIT).mp4 (74.0 MB) 004 Case Study-4 Demo of What-if Tool (WIT)_en.srt (10.4 KB) 005 Case Study-5 Demo of What-if Tool (WIT).mp4 (106.9 MB) 005 Case Study-5 Demo of What-if Tool (WIT)_en.srt (12.5 KB) external-assets-links.txt (0.3 KB) 07 - Layer-wise Relevance Propagation (LRP) 001 Interaction Demos of LR files
Added:	2022-01-21 10:06:21
Language:	English
Peers:	Seeders : 8 , Leechers : 18
Tags:	IT & Software Udemy
Release name:	Udemy - Explainable Artificial Intelligence (XAI) with Python
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